AI in multi-omics analysis in viral diseases

Area of Science:

• Virology
• Bioinformatics
• Computational Biology

Background:

• Viral diseases pose significant global health risks, causing epidemics and long-term complications.
• Understanding virus-host interactions is complex, requiring advanced analytical methods.
• Multi-omics datasets (genomics, transcriptomics, proteomics, etc.) offer comprehensive biological insights but are challenging to integrate.

Purpose of the Study:

• To explore the application of artificial intelligence (AI) in analyzing and interpreting multi-omics data for viral disease research.
• To highlight how AI-driven multi-omics approaches advance the understanding of viral pathogenesis and host responses.
• To discuss the potential of integrated multi-omics and AI for improved viral disease diagnosis, treatment, and prevention.

Main Methods:

• Review of studies applying AI, including machine learning (ML) and network-based approaches, to multi-omics data in viral research.
• Analysis of AI's role in integrating and interpreting high-dimensional datasets from various omics levels.
• Examination of case studies involving viruses such as SARS-CoV-2, HIV, and influenza.

Main Results:

• AI effectively analyzes, integrates, and interprets complex multi-omics data for viral disease studies.
• AI-assisted multi-omics approaches enhance understanding of virus-induced cellular changes.
• Identification of biomarkers and design of targeted therapies are facilitated by AI in viral research.

Conclusions:

• Integrating multi-omics data with AI significantly improves the diagnosis, treatment, and prevention of viral diseases.
• AI-powered multi-omics research offers deeper insights into virus-host interactions.
• Addressing challenges in data integration and AI methodology is crucial for future advancements.